A language model is tasked with classifying the sentiment of the input text: 'The plot was predictable, but the acting was superb.' The model is restricted to choosing a label from the set {positive, negative, neutral}. After processing the input, the model calculates the following conditional probabilities for each possible label:
- Pr(positive | input) = 0.45
- Pr(negative | input) = 0.20
- Pr(neutral | input) = 0.35
According to the principle of selecting the label that maximizes this probability, which label will the model output?
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
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A language model is tasked with classifying the sentiment of the input text: 'The plot was predictable, but the acting was superb.' The model is restricted to choosing a label from the set {positive, negative, neutral}. After processing the input, the model calculates the following conditional probabilities for each possible label:
- Pr(positive | input) = 0.45
- Pr(negative | input) = 0.20
- Pr(neutral | input) = 0.35
According to the principle of selecting the label that maximizes this probability, which label will the model output?
Analysis of a Model's Classification Decision
In a classification task, a model selects the most suitable label by using the formula:
label = argmax_{y ∈ Y} Pr(y|x). Match each component of this formula to its correct description.